OpenRaiser/Intern-Atlas huggingface dataset:
A special role of Boolean quadratic polytopes among other combinatorial polytopes
Venue: arXiv cs.CC 2015
Year: 2015

We consider several families of combinatorial polytopes associated with the following NP-complete problems: maximum cut, Boolean quadratic programming, quadratic linear ordering, quadratic assignment, set partition, set packing, stable set, 3-assignment. For comparing two families of polytopes we use the following method. We say that a family $P$ is affinely reduced to a family $Q$ if for every polytope $p\in P$ there exists $q\in Q$ such that $p$ is affinely equivalent to $q$ or to a face of $q$, where $\dim q = O((\dim p)^k)$ for some constant $k$. Under this comparison the above-mentioned families are splitted into two equivalence classes. We show also that these two classes are simpler (in the above sence) than the families of poytopes of the following problems: set covering, traveling salesman, 0-1 knapsack problem, 3-satisfiability, cubic subgraph, partial ordering. In particular, Boolean quadratic polytopes appear as faces of polytopes in every of the mentioned families.
Super-resolution, Extremal Functions and the Condition Number of Vandermonde Matrices
Venue: arXiv cs.IT 2015
Year: 2015

Super-resolution is a fundamental task in imaging, where the goal is to extract fine-grained structure from coarse-grained measurements. Here we are interested in a popular mathematical abstraction of this problem that has been widely studied in the statistics, signal processing and machine learning communities. We exactly resolve the threshold at which noisy super-resolution is possible. In particular, we establish a sharp phase transition for the relationship between the cutoff frequency ($m$) and the separation ($Δ$). If $m > 1/Δ+ 1$, our estimator converges to the true values at an inverse polynomial rate in terms of the magnitude of the noise. And when $m < (1-ε) /Δ$ no estimator can distinguish between a particular pair of $Δ$-separated signals even if the magnitude of the noise is exponentially small. Our results involve making novel connections between {\em extremal functions} and the spectral properties of Vandermonde matrices. We establish a sharp phase transition for their condition number which in turn allows us to give the first noise tolerance bounds for the matrix pencil method. Moreover we show that our methods can be interpreted as giving preconditioners for Vandermonde matrices, and we use this observation to design faster algorithms for super-resolution. We believe that these ideas may have other applications in designing faster algorithms for other basic tasks in signal processing.
A Semi-Decidable Procedure for Secrecy in Cryptographic Protocols
Venue: arXiv cs.CR 2015
Year: 2015

In this paper, we present a new semi-decidable procedure to analyze cryptographic protocols for secrecy based on a new class of functions that we call: the Witness-Functions. A Witness-Function is a reliable function that guarantees the secrecy in any protocol proved increasing once analyzed by it. Hence, the problem of correctness becomes a problem of protocol growth. A Witness-Function operates on derivative messages in a role-based specification and introduces new derivation techniques. We give here the technical aspects of the Witness-Functions and we show how to use them in a semi-decidable procedure. Then, we analyze a variation of the Needham-Schroeder protocol and we show that a Witness-Function can also help to teach about flaws. Finally, we analyze the NSL protocol and we prove that it is correct with respect to secrecy.
Distributed Estimation with Information-Seeking Control in Agent Network
Venue: arXiv cs.SY 2015
Year: 2015

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.
Learning Deep Representation for Face Alignment with Auxiliary Attributes
Venue: arXiv cs.CV 2015
Year: 2015

In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.
Distributed User Scheduling for MIMO-Y Channel
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, distributed user scheduling schemes are proposed for the multi-user MIMO-Y channel, where three $N_{T}$-antenna users ($N_{T}=2N,\,3N$) are selected from three clusters to exchange information via an $N_{R}$-antenna amplify-and-forward (AF) relay ($N_{R}=3N$), and $N\geq1$ represents the number of data stream(s) of each unicast transmission within the MIMO-Y channel. The proposed schemes effectively harvest multi-user diversity (MuD) without the need of global channel state information (CSI) or centralized computations. In particular, a novel reference signal space (RSS) is proposed to enable the distributed scheduling for both cluster-wise (CS) and group-wise (GS) patterns. The minimum user-antenna (Min-UA) transmission with $N_{T}=2N$ is first considered. Next, we consider an equal number of relay and user antenna (ER-UA) transmission with ${N_{T}=3N}$, with the aim of reducing CSI overhead as compared to Min-UA. For ER-UA transmission, the achievable MuD orders of the proposed distributed scheduling schemes are analytically derived, which proves the superiority and optimality of the proposed RSS-based distributed scheduling. These results reveal some fundamental behaviors of MuD and the performance-complexity tradeoff of user scheduling schemes in the MIMO-Y channel.
The Gaussian min-max theorem in the Presence of Convexity
Venue: arXiv cs.IT 2015
Year: 2015

Gaussian comparison theorems are useful tools in probability theory; they are essential ingredients in the classical proofs of many results in empirical processes and extreme value theory. More recently, they have been used extensively in the analysis of non-smooth optimization problems that arise in the recovery of structured signals from noisy linear observations. We refer to such problems as Primary Optimization (PO) problems. A prominent role in the study of the (PO) problems is played by Gordon's Gaussian min-max theorem (GMT) which provides probabilistic lower bounds on the optimal cost via a simpler Auxiliary Optimization (AO) problem. Motivated by resent work of M. Stojnic, we show that under appropriate convexity assumptions the (AO) problem allows one to tightly bound both the optimal cost, as well as the norm of the solution of the (PO). As an application, we use our result to develop a general framework to tightly characterize the performance (e.g. squared-error) of a wide class of convex optimization algorithms used in the context of noisy signal recovery.
A correspondence between rooted planar maps and normal planar lambda terms
Venue: arXiv cs.LO 2015
Year: 2015

A rooted planar map is a connected graph embedded in the 2-sphere, with one edge marked and assigned an orientation. A term of the pure lambda calculus is said to be linear if every variable is used exactly once, normal if it contains no beta-redexes, and planar if it is linear and the use of variables moreover follows a deterministic stack discipline. We begin by showing that the sequence counting normal planar lambda terms by a natural notion of size coincides with the sequence (originally computed by Tutte) counting rooted planar maps by number of edges. Next, we explain how to apply the machinery of string diagrams to derive a graphical language for normal planar lambda terms, extracted from the semantics of linear lambda calculus in symmetric monoidal closed categories equipped with a linear reflexive object or a linear reflexive pair. Finally, our main result is a size-preserving bijection between rooted planar maps and normal planar lambda terms, which we establish by explaining how Tutte decomposition of rooted planar maps (into vertex maps, maps with an isthmic root, and maps with a non-isthmic root) may be naturally replayed in linear lambda calculus, as certain surgeries on the string diagrams of normal planar lambda terms.
A Preconditioned Hybrid SVD Method for Computing Accurately Singular Triplets of Large Matrices
Venue: arXiv cs.NA 2015
Year: 2015

The computation of a few singular triplets of large, sparse matrices is a challenging task, especially when the smallest magnitude singular values are needed in high accuracy. Most recent efforts try to address this problem through variations of the Lanczos bidiagonalization method, but they are still challenged even for medium matrix sizes due to the difficulty of the problem. We propose a novel SVD approach that can take advantage of preconditioning and of any well designed eigensolver to compute both largest and smallest singular triplets. Accuracy and efficiency is achieved through a hybrid, two-stage meta-method, PHSVDS. In the first stage, PHSVDS solves the normal equations up to the best achievable accuracy. If further accuracy is required, the method switches automatically to an eigenvalue problem with the augmented matrix. Thus it combines the advantages of the two stages, faster convergence and accuracy, respectively. For the augmented matrix, solving the interior eigenvalue is facilitated by a proper use of the good initial guesses from the first stage and an efficient implementation of the refined projection method. We also discuss how to precondition PHSVDS and to cope with some issues that arise. Numerical experiments illustrate the efficiency and robustness of the method.
Enhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization
Venue: arXiv cs.IT 2015
Year: 2015

The mathematical theory of super-resolution developed recently by Candès and Fernandes-Granda states that a continuous, sparse frequency spectrum can be recovered with infinite precision via a (convex) atomic norm technique given a set of uniform time-space samples. This theory was then extended to the cases of partial/compressive samples and/or multiple measurement vectors via atomic norm minimization (ANM), known as off-grid/continuous compressed sensing (CCS). However, a major problem of existing atomic norm methods is that the frequencies can be recovered only if they are sufficiently separated, prohibiting commonly known high resolution. In this paper, a novel (nonconvex) sparse metric is proposed that promotes sparsity to a greater extent than the atomic norm. Using this metric an optimization problem is formulated and a locally convergent iterative algorithm is implemented. The algorithm iteratively carries out ANM with a sound reweighting strategy which enhances sparsity and resolution, and is termed as reweighted atomic-norm minimization (RAM). Extensive numerical simulations are carried out to demonstrate the advantageous performance of RAM with application to direction of arrival (DOA) estimation.
A Dichotomy of Functions in Distributed Coding: An Information Spectral Approach
Venue: arXiv cs.IT 2015
Year: 2015

The problem of distributed data compression for function computation is considered, where (i) the function to be computed is not necessarily symbol-wise function and (ii) the information source has memory and may not be stationary nor ergodic. We introduce the class of smooth sources and give a sufficient condition on functions so that the achievable rate region for computing coincides with the Slepian-Wolf region (i.e., the rate region for reproducing the entire source) for any smooth sources. Moreover, for symbol-wise functions, the necessary and sufficient condition for the coincidence is established. Our result for the full side-information case is a generalization of the result by Ahlswede and Csiszar to sources with memory; our dichotomy theorem is different from Han and Kobayashi's dichotomy theorem, which reveals an effect of memory in distributed function computation. All results are given not only for fixed-length coding but also for variable-length coding in a unified manner. Furthermore, for the full side-information case, the error probability in the moderate deviation regime is also investigated.
Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
Venue: arXiv cs.CR 2015
Year: 2015

To partly address people's concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user's profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.
Performance Comparison Between MIMO and SISO based on Indoor Field Measurements
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we quantify performance gain achieved if SISO system is replaced with 4x4 MIMO in WLAN setting compatible with IEEE 802.11n standard. We compare throughput and power savings in MIMO by taking field measurements at various indoor locations. Measurements are validated with simulations that include different IEEE TGn channel models. For comparison between MIMO and SISO, we select the Modulation and Coding Scheme (MCS) which yields the highest throughput subject to QoS constraints (PER <10%). We show that throughput gain of 2.5x-3x and power saving of 5-15 dB are achievable in 4x4 MIMO system.
Performance Analysis of Linear-Equality-Constrained Least-Squares Estimation
Venue: arXiv cs.PF 2015
Year: 2015

We analyze the performance of a linear-equality-constrained least-squares (CLS) algorithm and its relaxed version, called rCLS, that is obtained via the method of weighting. The rCLS algorithm solves an unconstrained least-squares problem that is augmented by incorporating a weighted form of the linear constraints. As a result, unlike the CLS algorithm, the rCLS algorithm is amenable to our approach to performance analysis presented here, which is akin to the energy-conservation-based methodology. Therefore, we initially inspect the convergence properties and evaluate the precision of estimation as well as satisfaction of the constraints for the rCLS algorithm in both mean and mean-square senses. Afterwards, we examine the performance of the CLS algorithm by evaluating the limiting performance of the rCLS algorithm as the relaxation parameter (weight) approaches infinity. Numerical examples verify the accuracy of the theoretical findings.
Software-Defined Cloud Computing: Architectural Elements and Open Challenges
Venue: arXiv cs.DC 2015
Year: 2015

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.
Integrated structure investigation in complex networks by label propagation
Venue: arXiv cs.SI 2015
Year: 2015

The investigation of network structure has important significance to understand the functions of various complex networks. The communities with hierarchical and overlapping structures and the special nodes like hubs and outliers are all common structure features to the networks. Network structure investigation has attracted considerable research effort recently. However, existing studies have only partially explored the structure features. In this paper, a label propagation based integrated network structure investigation algorithm (LINSIA) is proposed. The main novelty here is that LINSIA can uncover hierarchical and overlapping communities, as well as hubs and outliers. Moreover, LINSIA can provide insight into the label propagation mechanism and propose a parameter-free solution that requires no prior knowledge. In addition, LINSIA can give out a soft-partitioning result and depict the degree of overlapping nodes belonging to each relevant community. The proposed algorithm is validated on various synthetic and real-world networks. Experimental results demonstrate that the algorithm outperforms several state-of-the-art methods.
Balanced permutations Even-Mansour ciphers
Venue: arXiv cs.CR 2015
Year: 2015

The $r$-rounds Even-Mansour block cipher is a generalization of the well known Even-Mansour block cipher to $r$ iterations. Attacks on this construction were described by Nikolić et al. and Dinur et al., for $r = 2, 3$. These attacks are only marginally better than brute force, but are based on an interesting observation (due to Nikolić et al.): for a "typical" permutation $P$, the distribution of $P(x) \oplus x$ is not uniform. This naturally raises the following question. Call permutations for which the distribution of $P(x) \oplus x$ is uniform "balanced." Is there a sufficiently large family of balanced permutations, and what is the security of the resulting Even-Mansour block cipher? We show how to generate families of balanced permutations from the Luby-Rackoff construction, and use them to define a $2n$-bit block cipher from the $2$-rounds Even-Mansour scheme. We prove that this cipher is indistinguishable from a random permutation of $\{0, 1\}^{2n}$, for any adversary who has oracle access to the public permutations and to an encryption/decryption oracle, as long as the number of queries is $o (2^{n/2})$. As a practical example, we discuss the properties and the performance of a $256$-bit block cipher that is based on our construction, and uses AES as the public permutation.
Distortion Exponent in MIMO Fading Channels with Time-Varying Source Side Information
Venue: arXiv cs.IT 2015
Year: 2015

Transmission of a Gaussian source over a time-varying multiple-input multiple-output (MIMO) channel is studied under strict delay constraints. Availability of a correlated side information at the receiver is assumed, whose quality, i.e., correlation with the source signal, also varies over time. A block-fading model is considered for the states of the time-varying channel and the time-varying side information; and perfect state information at the receiver is assumed, while the transmitter knows only the statistics. The high SNR performance, characterized by the \textit{distortion exponent}, is studied for this joint source-channel coding problem. An upper bound is derived and compared with lowers based on list decoding, hybrid digital-analog transmission, as well as multi-layer schemes which transmit successive refinements of the source, relying on progressive and superposed transmission with list decoding. The optimal distortion exponent is characterized for the single-input multiple-output (SIMO) and multiple-input single-output (MISO) scenarios by showing that the distortion exponent achieved by multi-layer superpositon encoding with joint decoding meets the proposed upper bound. In the MIMO scenario, the optimal distortion exponent is characterized in the low bandwidth ratio regime, and it is shown that the multi-layer superposition encoding performs very close to the upper bound in the high bandwidth expansion regime.
Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes
Venue: arXiv cs.SY 2015
Year: 2015

This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces. The models further depend on a non-deterministic quantity in the form of a control input, which can be selected to affect the probabilistic dynamics. We address the computation of maximal reach-avoid specifications, together with the synthesis of the corresponding optimal controllers. The reach-avoid specification deals with assessing the likelihood that any finite-horizon trajectory of the model enters a given goal set, while avoiding a given set of undesired states. This article newly provides an approximate computational scheme for the reach-avoid specification based on the Fitted Value Iteration algorithm, which hinges on random sample extractions, and gives a-priori computable formal probabilistic bounds on the error made by the approximation algorithm: as such, the output of the numerical scheme is quantitatively assessed and thus meaningful for safety-critical applications. Furthermore, we provide tighter probabilistic error bounds that are sample-based. The overall computational scheme is put in relationship with alternative approximation algorithms in the literature, and finally its performance is practically assessed over a benchmark case study.